## [1] "/Users/victoriaqc/Documents/GitHub/PFAS_met_cage"
Introduction
Start with Plate Reader Spreadsheet
View in Excel vs. View in R
test <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Albuwell_061225.xlsx"))
# isolate to plate
subset_df <- test[9:16, 1:9]
The subset of the data looks like this
This is what the tidy data looks like
1. Load Sample Info and Merge the data
# Load Sample Info
df.Sample <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Long_PFAS_Albuwell_Sample_061225.xlsx"))
tidy_data <- tidy_df %>%
left_join(df.Sample, by = c("Cell" = "Cell"))
a. Graph the Ladder
Graph ladder and calculate the curve fit
# filter if Sample is Ladder or Blank
df.Ladder <- tidy_data %>%
filter(Sample == "Ladder" | Sample == "Blank")
# Subtract Blank and remove the Blank row
df.Ladder2 <- df.Ladder %>%
mutate(Value2 = ifelse(Sample != "Blank", Value - filter(df.Ladder, Sample == "Blank")$Value, NA)) %>%
filter(!is.na(Value2))
# Create a scatter plot of df.Ladder2
ggplot(df.Ladder2, aes(x = Ladder_Value, y = Value2, color = Sample)) +
geom_point() +
theme_minimal() +
labs(title = "Ladder",
x = "Cell",
y = "Value",
color = "Sample")
# curve fit the data using a natural log
x <- df.Ladder2$Ladder_Value
y <- df.Ladder2$Value2
# Fit the non-linear regression model
fit <- nls(y ~ a + b * log(x), start = list(a = 1, b = 1))
# Print summary of the non-linear regression
summary(fit)
##
## Formula: y ~ a + b * log(x)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 0.89423 0.02068 43.25 1.52e-14 ***
## b -0.24175 0.01472 -16.42 1.38e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07638 on 12 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.024e-07
# Extract coefficients
coefficients <- coef(fit)
m <- as.numeric(coefficients[2])
b <- as.numeric(coefficients[1])
# Calculate predicted values
predicted <- predict(fit, list(x = x))
# Calculate R-squared
residuals <- y - predicted
rss <- sum(residuals^2)
tss <- sum((y - mean(y))^2)
r_squared <- 1 - rss/tss
# Create data for plotting
plot_data <- data.frame(x = x, y = y, Predicted = predicted)
# Create the ggplot with scatter plot and fitted line
p <- ggplot(plot_data, aes(x = x, y = y)) +
geom_point(color = "darkred", size = 5) + # Scatter plot of x vs y
geom_line(aes(y = Predicted), color = "red") + # Fitted line
labs(
title = paste0("Scatter plot with Non-linear Logarithmic Fit: y =", round(m, 3), "ln(x) +", round(b, 3), "\nR² =", round(r_squared, 3)),
x = "Albumin Concentration",
y = "Absorbance"
) +
theme_classic()
# Print the plot
print(p)
Calcuate back the latter values based on the fit equation
# Subtract Blank and calculate the values
tidy_data_blank <- tidy_data %>%
mutate(Value2 = ifelse(Sample != "Blank", Value - filter(df.Ladder, Sample == "Blank")$Value, NA)) %>%
filter(!is.na(Value2))
tidy_data_solved <- tidy_data_blank %>%
mutate(Solved = exp((Value2 - b) / m))
df_test <- tidy_data_solved %>%
filter(Sample == "Ladder")
# Create a scatter plot of df_test
ggplot(df_test, aes(x = Ladder_Value, y = Solved, color = as.character(Ladder_Value))) +
geom_point(size = 5) +
theme_minimal() +
labs(title = "Checking Equation Fit Against Ladder",
x = "Sample",
y = "Value",
color = "Cell")
Graph the sample data on the solved curve split by dilution
factor
df_sample_test <- tidy_data_solved %>%
filter(Type == "Sample")
df_sample_test
p + geom_point(data = df_sample_test, aes(x = Solved, y = Value2, color = as.character(Dilution)), size = 3) +
labs(color = "Dilution") # Change the legend title to "Dilution"
Calculate with Dilution Factor
tidy_data_solved_dil <- tidy_data_solved %>%
mutate(Concentration = Solved * Dilution)
# createa scatterplot of tidy_data_solved_dil of the Concentration that colors by dilution
ggplot(tidy_data_solved_dil, aes(x = Sample, y = Concentration, color = as.character(Replicate))) +
geom_point(size = 3) +
theme_minimal() +
labs(
title = "Concentration of Albumin by Dilution",
x = "Dilution", # Change if "Sample" is not "Cell"
y = "Concentration (µg/mL)",
color = "Replicate"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# create a new column that measures the mean of the values for each sample
tidy_data_solved_dil2 <- tidy_data_solved_dil %>%
group_by(Sample) %>%
mutate(Mean = mean(Concentration, na.rm = TRUE))
ggplot(tidy_data_solved_dil2, aes(x = Sample, y = Mean, color = as.character(Dilution))) +
geom_point(size = 3) +
theme_minimal() +
labs(
title = "Concentration of Albumin by Dilution",
x = "Dilution", # Or "Sample", based on context
y = "Mean Albumin Concentration (µg/mL)",
color = "Dilution"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
3. Read in Creatinine Values
df_creatinine <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Creatinine061825.xlsx"))
subset_pre <- df_creatinine[19:25, 2:10]
# rename the columns to 1-12
colnames(subset_pre) <- c("Row", 1:8)
subset_pre <- subset_pre %>%
mutate(across(-Row, as.numeric))
tidy_df_pre <- subset_pre %>%
pivot_longer(cols = -Row, # Pivot all columns except Row
names_to = "Column", # Name of new column for the old column names
values_to = "Value") %>% # Name of new column for the values
mutate(Cell = paste0(Row, Column))%>% # Create the new Cell column
select(-Row, -Column) %>%
select(Cell, Value)
as.character(colnames(subset_pre))
## [1] "Row" "1" "2" "3" "4" "5" "6" "7" "8"
#set the colnames as characters
df_creatinine_post <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Creatinine_Acid052125.xlsx"))
subset_post <- df_creatinine_post[9:15, 1:9]
# rename the columns to 1-12
colnames(subset_post) <- c("Row", 1:8)
subset_post <- subset_post %>%
mutate(across(-Row, as.numeric))
tidy_df_post <- subset_post %>%
pivot_longer(cols = -Row, # Pivot all columns except Row
names_to = "Column", # Name of new column for the old column names
values_to = "Value") %>% # Name of new column for the values
mutate(Cell = paste0(Row, Column))%>% # Create the new Cell column
select(-Row, -Column) %>%
select(Cell, Value)
# merge tidy_df_pre and tidy_df_post by Row
tidy_df <- merge(tidy_df_pre, tidy_df_post, by = "Cell")
tidy_df <- tidy_df %>%
mutate(Value = Value.x - Value.y) %>%
select(Cell, Value)
Load Sample Info and Merge the data
# Load Sample Info
df.Sample <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Creatinine_Sample_061825.xlsx"))
tidy_data <- tidy_df %>%
left_join(df.Sample, by = c("Cell" = "Cell"))
a. Graph the Ladder
Graph ladder and calculate the curve fit for creatinine
# filter if Sample is Ladder or Blank
df.Ladder_Cre <- tidy_data %>%
filter(Sample == "Ladder")
ggplot(df.Ladder_Cre, aes(x = Ladder_Value, y = Value, color = Sample)) +
geom_point() +
theme_minimal() +
labs(title = "Ladder",
x = "Cell",
y = "Value",
color = "Sample")
# curve fit the data using a natural log
x <- df.Ladder_Cre$Ladder_Value
y <- df.Ladder_Cre$Value
fit <- lm(Value ~ Ladder_Value, data = df.Ladder_Cre)
# Print summary of the non-linear regression
summary(fit)
##
## Call:
## lm(formula = Value ~ Ladder_Value, data = df.Ladder_Cre)
##
## Residuals:
## 1 2 3 4 5 6
## 0.002612 -0.002388 0.005496 -0.006504 0.002892 -0.002108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0004104 0.0031685 0.13 0.903
## Ladder_Value 0.0556978 0.0005233 106.44 4.67e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.004946 on 4 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.133e+04 on 1 and 4 DF, p-value: 4.671e-08
coefficients <- coef(fit)
m <- coefficients["Ladder_Value"]
b <- coefficients["(Intercept)"]
plot_data <- data.frame(x = x, y = y, Predicted = predict(fit, newdata = data.frame(Ladder_Value = x)))
# Create the ggplot with scatter plot and fitted line
p <- ggplot(plot_data, aes(x = x, y = y)) +
geom_point() + # Scatter plot of x vs y
geom_line(aes(y = Predicted), color = "red") + # Fitted line
labs(
title = paste0("Scatter plot with Non-linear Logarithmic Fit: y =", round(m, 3), "ln(x) +", round(b, 3)),
x = "Creatinine Concentration",
y = "Absorbance"
) +
theme_classic() +
geom_point(color = "darkred", size = 5)
p
b. Calcuate the values from the samples
# Subtract Blank and calculate the values
tidy_data_blank <- tidy_data %>%
mutate(Value = ifelse(Sample != "Blank", Value - filter(df.Ladder, Sample == "Blank")$Value, NA)) %>%
filter(!is.na(Value))
tidy_data_blank <- tidy_data %>%
mutate(Value = Value) %>%
filter(!is.na(Value))
tidy_data_solved <- tidy_data_blank %>%
mutate(Solved = ((Value - b) / m))
df_test <- tidy_data_solved %>%
filter(Sample == "Ladder")
# Create a scatter plot of df_test
ggplot(df_test, aes(x = Ladder_Value, y = Solved, color = as.character(Ladder_Value))) +
geom_point() +
theme_minimal() +
labs(title = "Checking Equation Fit Against Ladder",
x = "Sample",
y = "Value",
color = "Cell")
df_sample_test <- tidy_data_solved %>%
filter(Sample != "Blank" | Sample != "Ladder"| Sample != "Max")
# filter tidy_data_solved to not include Blank, Max, or Ladder in the Sample column
df_sample_test
p + geom_point(data = df_sample_test, aes(x = Solved, y = Value), color = as.character(df_sample_test$Dilution), size = 3)
# If Dilution is greater than 10 then write (Above), if the not write OK
df_sample_test <- df_sample_test %>%
mutate(Dilution_calc = ifelse(Solved < 1, "Below", "OK"))
c. Calculate with Dilution Factor
tidy_data_solved <- tidy_data_solved %>%
mutate(Dilution_calc = ifelse(Solved < 1, "Below", "OK"))
tidy_data_solved_dil3 <- tidy_data_solved %>%
mutate(Concentration = Solved * Dilution) %>%
filter(!is.na(Concentration))
# create a scatterplot of tidy_data_solved_dil of the Concentration that colors by dilution
ggplot(tidy_data_solved_dil3, aes(x = Sample, y = Concentration, color = as.character(Dilution))) +
geom_point() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Concentration of Creatinine by Dilution",
x = "Cell",
y = "Concentration",
color = "Dilution")
ggplot(tidy_data_solved_dil3, aes(x = Sample, y = Concentration, color = Dilution_calc)) +
geom_point() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Concentration of Creatinine by Dilution",
x = "Cell",
y = "Concentration",
color = "Dilution")
table <- tidy_data_solved_dil3 %>%
group_by(Dilution, Dilution_calc) %>%
summarize(count = n())
table
creatinine_table <- tidy_data_solved_dil3 %>%
filter(Dilution == "5") %>%
group_by(Sample) %>%
summarize(Cre_Concentration = mean(Concentration))
#convert grams to dL for urine
tidy_data_solved_dil_meta$Urine_dL <- tidy_data_solved_dil_meta$UV * 0.01
#creatinine mass
tidy_data_solved_dil_meta$Creatinine_Mass <- tidy_data_solved_dil_meta$Urine_dL * tidy_data_solved_dil_meta$Concentration
tidy_data_solved_dil_meta <- tidy_data_solved_dil_meta[!is.na(tidy_data_solved_dil_meta$Treatment), ]
tidy_data_solved_dil_meta$Treatment <- factor(tidy_data_solved_dil_meta$Treatment,
levels = c("Vehicle", "1 mg", "10 mg")
)
#Dot plot for Creatinine Mass
tidy_data_solved_dil_meta$`Timepoint Treatment` <- factor(tidy_data_solved_dil_meta$`Timepoint Treatment`,
levels = c("Baseline Vehicle", "Final Vehicle", "Baseline 1mg", "Final 1mg", "Baseline 10mg", "Final 10mg")
)
ggplot(tidy_data_solved_dil_meta, aes(x = `Timepoint Treatment`, y = Creatinine_Mass, color = Timepoint)) +
geom_point(size = 3) +
labs(title = "Creatinine Mass at Baseline and Endpoint by Treatment Group",
x = "Treatment Group",
y = "Creatinine Mass (g/dL)",
color = "Treatment")
4. Calculate ACR
df.meta <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Long_PFAS_Metadata.xlsx"), colNames = F, rowNames = T)
df.ACR <- merge(creatinine_table, albumin_table, by = "Sample")
df.ACR <- df.ACR %>%
mutate(Alb_mg_dL = Alb_Concentration / 10) %>%
mutate(Cre_g_dL = Cre_Concentration / 1000) %>%
mutate(ACR_mg_g = Alb_mg_dL / Cre_g_dL)
df.ACR
a. Graph of ACR by group
ACR_meta <- tidy_data_solved_dil_meta %>%
left_join(df.ACR, by = c("Sample" = "Sample"))
levels(df.ACR$'Timepoint Treatment')
## NULL
levels(ACR_meta$'Timepoint Treatment')
## [1] "Baseline Vehicle" "Final Vehicle" "Baseline 1mg" "Final 1mg"
## [5] "Baseline 10mg" "Final 10mg"
ACR_meta$`Timepoint Treatment` <- as.factor(ACR_meta$`Timepoint Treatment`)
ACR_meta$`Timepoint Treatment` <- factor(ACR_meta$`Timepoint Treatment`,
levels = c("Baseline Vehicle", "Final Vehicle", "Baseline 1mg", "Final 1mg", "Baseline 10mg", "Final 10mg")
)
ggplot(ACR_meta, aes(x = `Timepoint Treatment`, y = ACR_mg_g, color = as.character(Treatment))) +
geom_point(size = 3) +
theme_minimal() +
labs(title = "ACR by Treatment",
x = "Timepoint Treatment",
y = "ACR",
color = "Treatment")
5. Load Sample Info and Merge the data (Week)
test_week <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Albuwell_Week_070325.xlsx"))
# isolate to plate
subset_df_week <- test_week[9:16, 1:12]
# rename the columns to 1-11
colnames(subset_df_week) <- c("Row", 1:11)
subset_df_week %>%
mutate(across(-Row, as.numeric)) %>% # or use as.character if preferred
pivot_longer(cols = -Row, names_to = "Column", values_to = "Value")
clean_df_week <- subset_df_week %>%
mutate(across(-Row, as.numeric)) # or as.character if needed
tidy_df_week <- clean_df_week %>%
pivot_longer(cols = -Row,
names_to = "Column",
values_to = "Value") %>%
mutate(Cell = paste0(Row, Column)) %>%
select(Cell, Value)
df.Sample_week <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Long_PFAS_Albuwell_Sample_Week_070325.xlsx"))
tidy_data_week <- tidy_df_week %>%
left_join(df.Sample_week, by = c("Cell" = "Cell"))
a. Graph the Ladder
Graph ladder and calculate the curve fit
# filter if Sample is Ladder or Blank
df.Ladder_week <- tidy_data_week %>%
filter(Sample == "Ladder" | Sample == "Blank")
# Subtract Blank and remove the Blank row
df.Ladder2_week <- df.Ladder_week %>%
mutate(Value2 = ifelse(Sample != "Blank", Value - filter(df.Ladder, Sample == "Blank")$Value, NA)) %>%
filter(!is.na(Value2))
# Create a scatter plot of df.Ladder2
ggplot(df.Ladder2_week, aes(x = Ladder_Value, y = Value2, color = Sample)) +
geom_point() +
theme_minimal() +
labs(title = "Ladder",
x = "Cell",
y = "Value",
color = "Sample")
# curve fit the data using a natural log
x <- df.Ladder2_week$Ladder_Value
y <- df.Ladder2_week$Value2
# Fit the non-linear regression model
fit <- nls(y ~ a + b * log(x), start = list(a = 1, b = 1))
# Print summary of the non-linear regression
summary(fit)
##
## Formula: y ~ a + b * log(x)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 1.02643 0.02426 42.3 1.98e-14 ***
## b -0.25579 0.01728 -14.8 4.52e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08964 on 12 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 9.19e-08
# Extract coefficients
coefficients <- coef(fit)
m <- as.numeric(coefficients[2])
b <- as.numeric(coefficients[1])
# Calculate predicted values
predicted <- predict(fit, list(x = x))
# Calculate R-squared
residuals <- y - predicted
rss <- sum(residuals^2)
tss <- sum((y - mean(y))^2)
r_squared <- 1 - rss/tss
# Create data for plotting
plot_data_week <- data.frame(x = x, y = y, Predicted = predicted)
# Create the ggplot with scatter plot and fitted line
p <- ggplot(plot_data_week, aes(x = x, y = y)) +
geom_point(color = "darkred", size = 5) + # Scatter plot of x vs y
geom_line(aes(y = Predicted), color = "red") + # Fitted line
labs(
title = paste0("Scatter plot with Non-linear Logarithmic Fit: y =", round(m, 3), "ln(x) +", round(b, 3), "\nR² =", round(r_squared, 3)),
x = "Albumin Concentration",
y = "Absorbance"
) +
theme_classic()
# Print the plot
print(p)
Calcuate back the latter values based on the fit equation
# Subtract Blank and calculate the values
tidy_data_blank_week <- tidy_data_week %>%
mutate(Value2 = ifelse(Sample != "Blank", Value - filter(df.Ladder, Sample == "Blank")$Value, NA)) %>%
filter(!is.na(Value2))
tidy_data_solved_week <- tidy_data_blank_week %>%
mutate(Solved = exp((Value2 - b) / m))
df_test_week <- tidy_data_solved_week %>%
filter(Sample == "Ladder")
# Create a scatter plot of df_test
ggplot(df_test_week, aes(x = Ladder_Value, y = Solved, color = as.character(Ladder_Value))) +
geom_point(size = 5) +
theme_minimal() +
labs(title = "Checking Equation Fit Against Ladder",
x = "Sample",
y = "Value",
color = "Cell")
Graph the sample data on the solved curve split by dilution
factor
df_sample_test_week <- tidy_data_solved_week %>%
filter(Type == "Sample")
df_sample_test_week
p + geom_point(data = df_sample_test_week, aes(x = Solved, y = Value2, color = as.character(Dilution)), size = 3) +
labs(color = "Dilution") # Change the legend title to "Dilution"
Calculate with Dilution Factor
tidy_data_solved_dil_week <- tidy_data_solved_week %>%
mutate(Concentration = Solved * Dilution)
# createa scatterplot of tidy_data_solved_dil of the Concentration that colors by dilution
ggplot(tidy_data_solved_dil_week, aes(x = Sample, y = Concentration, color = as.character(Replicate))) +
geom_point(size = 3) +
theme_minimal() +
labs(
title = "Concentration of Albumin by Dilution",
x = "Dilution", # Change if "Sample" is not "Cell"
y = "Concentration (µg/mL)",
color = "Replicate"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# create a new column that measures the mean of the values for each sample
tidy_data_solved_dil2_week <- tidy_data_solved_dil_week %>%
group_by(Sample) %>%
mutate(Mean = mean(Concentration, na.rm = TRUE))
ggplot(tidy_data_solved_dil2_week, aes(x = Sample, y = Mean, color = as.character(Dilution))) +
geom_point(size = 3) +
theme_minimal() +
labs(
title = "Concentration of Albumin by Dilution",
x = "Dilution", # Or "Sample", based on context
y = "Mean Albumin Concentration (µg/mL)",
color = "Dilution"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
7. Read Creatinine (By Week)
df_creatinine_week <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Creatinine_Week_070325.xlsx"))
subset_pre_week <- df_creatinine_week[9:16, 1:12]
# rename the columns to 1-12
colnames(subset_pre_week) <- c("Row", 1:11)
subset_pre_week <- subset_pre_week %>%
mutate(across(-Row, as.numeric))
tidy_df_pre_week <- subset_pre_week %>%
pivot_longer(cols = -Row, # Pivot all columns except Row
names_to = "Column", # Name of new column for the old column names
values_to = "Value") %>% # Name of new column for the values
mutate(Cell = paste0(Row, Column))%>% # Create the new Cell column
select(-Row, -Column) %>%
select(Cell, Value)
as.character(colnames(subset_pre_week))
## [1] "Row" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11"
#set the colnames as characters
df_creatinine_post_week <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Creatinine_Acid_Week_070325.xlsx"))
subset_post_week <- df_creatinine_post_week[9:16, 1:12]
# rename the columns to 1-12
colnames(subset_post_week) <- c("Row", 1:11)
subset_post_week <- subset_post_week %>%
mutate(across(-Row, as.numeric))
tidy_df_post_week <- subset_post_week %>%
pivot_longer(cols = -Row, # Pivot all columns except Row
names_to = "Column", # Name of new column for the old column names
values_to = "Value") %>% # Name of new column for the values
mutate(Cell = paste0(Row, Column))%>% # Create the new Cell column
select(-Row, -Column) %>%
select(Cell, Value)
# merge tidy_df_pre and tidy_df_post by Row
tidy_df_week <- merge(tidy_df_pre_week, tidy_df_post_week, by = "Cell")
tidy_df_week <- tidy_df_week %>%
mutate(Value = Value.x - Value.y) %>%
select(Cell, Value)
Load Sample Info and Merge the data
# Load Sample Info
df.Sample_week <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Creatinine_Sample_Week_070325.xlsx"))
tidy_data_week <- tidy_df_week %>%
left_join(df.Sample_week, by = c("Cell" = "Cell"))
a. Graph the Ladder
Graph ladder and calculate the curve fit for creatinine
# filter if Sample is Ladder or Blank
df.Ladder_Cre_week <- tidy_data_week %>%
filter(Sample == "Ladder")
ggplot(df.Ladder_Cre_week, aes(x = Ladder_Value, y = Value, color = Sample)) +
geom_point() +
theme_minimal() +
labs(title = "Ladder",
x = "Cell",
y = "Value",
color = "Sample")
# curve fit the data using a natural log
x <- df.Ladder_Cre_week$Ladder_Value
y <- df.Ladder_Cre_week$Value
fit <- lm(Value ~ Ladder_Value, data = df.Ladder_Cre_week)
# Print summary of the non-linear regression
summary(fit)
##
## Call:
## lm(formula = Value ~ Ladder_Value, data = df.Ladder_Cre_week)
##
## Residuals:
## 1 2 3 4 5 6
## -0.027993 0.026007 0.006966 0.001966 -0.004474 -0.002474
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.019194 0.012565 1.528 0.201
## Ladder_Value 0.045280 0.002075 21.822 2.61e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01961 on 4 degrees of freedom
## Multiple R-squared: 0.9917, Adjusted R-squared: 0.9896
## F-statistic: 476.2 on 1 and 4 DF, p-value: 2.609e-05
coefficients <- coef(fit)
m <- coefficients["Ladder_Value"]
b <- coefficients["(Intercept)"]
plot_data_week <- data.frame(x = x, y = y, Predicted = predict(fit, newdata = data.frame(Ladder_Value = x)))
# Create the ggplot with scatter plot and fitted line
p <- ggplot(plot_data_week, aes(x = x, y = y)) +
geom_point() + # Scatter plot of x vs y
geom_line(aes(y = Predicted), color = "red") + # Fitted line
labs(
title = paste0("Scatter plot with Non-linear Logarithmic Fit: y =", round(m, 3), "ln(x) +", round(b, 3)),
x = "Creatinine Concentration",
y = "Absorbance"
) +
theme_classic() +
geom_point(color = "darkred", size = 5)
p
b. Calcuate the values from the samples
# Subtract Blank and calculate the values
tidy_data_blank_week <- tidy_data_week %>%
mutate(Value = ifelse(Sample != "Blank", Value - filter(df.Ladder, Sample == "Blank")$Value, NA)) %>%
filter(!is.na(Value))
tidy_data_blank_week <- tidy_data_week %>%
mutate(Value = Value) %>%
filter(!is.na(Value))
tidy_data_solved_week <- tidy_data_blank_week %>%
mutate(Solved = ((Value - b) / m))
df_test_week <- tidy_data_solved_week %>%
filter(Sample == "Ladder")
# Create a scatter plot of df_test
ggplot(df_test_week, aes(x = Ladder_Value, y = Solved, color = as.character(Ladder_Value))) +
geom_point() +
theme_minimal() +
labs(title = "Checking Equation Fit Against Ladder",
x = "Sample",
y = "Value",
color = "Cell")
df_sample_test_week <- tidy_data_solved_week %>%
filter(Sample != "Blank" | Sample != "Ladder"| Sample != "Max")
# filter tidy_data_solved to not include Blank, Max, or Ladder in the Sample column
df_sample_test_week
p + geom_point(data = df_sample_test_week, aes(x = Solved, y = Value), color = as.character(df_sample_test_week$Dilution), size = 3)
# If Dilution is greater than 10 then write (Above), if the not write OK
df_sample_test_week <- df_sample_test_week %>%
mutate(Dilution_calc = ifelse(Solved < 1, "Below", "OK"))
c. Calculate with Dilution Factor
tidy_data_solved_week <- tidy_data_solved_week %>%
mutate(Dilution_calc = ifelse(Solved < 1, "Below", "OK"))
tidy_data_solved_dil3_week <- tidy_data_solved_week %>%
mutate(Concentration = Solved * Dilution) %>%
filter(!is.na(Concentration))
# create a scatterplot of tidy_data_solved_dil of the Concentration that colors by dilution
ggplot(tidy_data_solved_dil3_week, aes(x = Sample, y = Concentration, color = as.character(Dilution))) +
geom_point() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Concentration of Creatinine by Dilution",
x = "Cell",
y = "Concentration",
color = "Dilution")
ggplot(tidy_data_solved_dil3_week, aes(x = Sample, y = Concentration, color = Dilution_calc)) +
geom_point() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Concentration of Creatinine by Dilution",
x = "Cell",
y = "Concentration",
color = "Dilution")
table_week <- tidy_data_solved_dil3_week %>%
group_by(Dilution, Dilution_calc) %>%
summarize(count = n())
table_week
creatinine_table_week <- tidy_data_solved_dil3_week %>%
filter(Dilution == "5") %>%
group_by(Sample) %>%
summarize(Cre_Concentration = mean(Concentration))
8. Calculate ACR (Week)
df.meta_week <- read.xlsx(here("VQC R work", "Albuwell Data Sheets", "Long_PFAS_Metadata_Week.xlsx"), colNames = F, rowNames = T)
df.ACR_week <- merge(creatinine_table_week, albumin_table_week, by = "Sample")
df.ACR_week <- df.ACR_week %>%
mutate(Alb_mg_dL = Alb_Concentration / 10) %>%
mutate(Cre_g_dL = Cre_Concentration / 1000) %>%
mutate(ACR_mg_g = Alb_mg_dL / Cre_g_dL)
df.ACR_week
a. Graph of ACR by group
ACR_meta_week <- tidy_data_solved_dil_meta_week %>%
left_join(df.ACR_week, by = c("Sample" = "Sample"))
levels(df.ACR_week$'Timepoint Treatment')
## NULL
levels(ACR_meta_week$'Timepoint Treatment')
## NULL
ACR_meta_week$`Timepoint Treatment` <- as.factor(ACR_meta_week$`Timepoint Treatment`)
ACR_meta_week$`Timepoint Treatment` <- factor(ACR_meta_week$`Timepoint Treatment`,
levels = c("Week 0 1 mg", "Week 1 1 mg", "Week 2 1 mg", "Week 3 1 mg", "Week 4 1 mg", "Week 0 10 mg", "Week 1 10 mg", "Week 2 10 mg", "Week 3 10 mg")
)
ggplot(ACR_meta_week, aes(x = `Timepoint Treatment`, y = ACR_mg_g, color = Treatment)) +
geom_point(size = 3) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
labs(title = "ACR by Treatment",
x = "Timepoint Treatment",
y = "ACR",
color = "Treatment")
## $x
## [1] "Timepoint Treatment"
##
## $y
## [1] "ACR"
##
## $colour
## [1] "Treatment"
##
## $title
## [1] "ACR by Treatment"
##
## attr(,"class")
## [1] "labels"
Session Info
## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.0
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidyr_1.3.1 ggplotify_0.1.2 pheatmap_1.0.12 openxlsx_4.2.7.1
## [5] dichromat_2.0-0.1 here_1.0.1 ggrepel_0.9.6 RColorBrewer_1.1-3
## [9] ggpmisc_0.6.1 ggpp_0.5.8-1 tibble_3.2.1 ggplot2_3.5.1
## [13] knitr_1.49 patchwork_1.3.0 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] yulab.utils_0.1.8 sass_0.4.9 utf8_1.2.4 generics_0.1.3
## [5] stringi_1.8.4 lattice_0.22-6 digest_0.6.37 magrittr_2.0.3
## [9] evaluate_1.0.1 grid_4.4.1 fastmap_1.2.0 rprojroot_2.0.4
## [13] jsonlite_1.8.9 Matrix_1.7-0 zip_2.3.1 survival_3.6-4
## [17] purrr_1.0.2 fansi_1.0.6 scales_1.3.0 jquerylib_0.1.4
## [21] cli_3.6.3 rlang_1.1.4 munsell_0.5.1 splines_4.4.1
## [25] withr_3.0.1 cachem_1.1.0 yaml_2.3.10 tools_4.4.1
## [29] SparseM_1.84-2 polynom_1.4-1 MatrixModels_0.5-3 colorspace_2.1-1
## [33] gridGraphics_0.5-1 vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4
## [37] fs_1.6.5 MASS_7.3-60.2 pkgconfig_2.0.3 pillar_1.9.0
## [41] bslib_0.8.0 gtable_0.3.5 glue_1.8.0 Rcpp_1.0.13
## [45] xfun_0.49 tidyselect_1.2.1 rstudioapi_0.17.1 farver_2.1.2
## [49] htmltools_0.5.8.1 labeling_0.4.3 rmarkdown_2.29 compiler_4.4.1
## [53] quantreg_5.99.1
